Seam-Carving forgery detection based on expanded Markov features

To deal with the digital image forgery using Seam-Carving, a detection algorithm based on expanded Markov features was proposed. The algorithm takes full acount of the image frequency domain change caused by Seam-Carving operation, merges the features based on traditional and expands Markov transfer...

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Main Authors: Guo-rui SHENG, Tie-gang GAO, Li FAN, Lin GAO, Fu-sheng YANG, Shun ZHANG
Format: Article
Language:zho
Published: Editorial Department of Journal on Communications 2014-06-01
Series:Tongxin xuebao
Subjects:
Online Access:http://www.joconline.com.cn/thesisDetails#10.3969/j.issn.1000-436x.2014.06.006
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author Guo-rui SHENG
Tie-gang GAO
Li FAN
Lin GAO
Fu-sheng YANG
Shun ZHANG
author_facet Guo-rui SHENG
Tie-gang GAO
Li FAN
Lin GAO
Fu-sheng YANG
Shun ZHANG
author_sort Guo-rui SHENG
collection DOAJ
description To deal with the digital image forgery using Seam-Carving, a detection algorithm based on expanded Markov features was proposed. The algorithm takes full acount of the image frequency domain change caused by Seam-Carving operation, merges the features based on traditional and expands Markov transfer-probability matrix. Trained by SVM, the merged Markov feature can identify the Seam-Carving forgery more effectively. The experiment result shows that the performance of the proposed method is better than that of the method based on traditional Markov features and other existing methods.
format Article
id doaj-art-ffde13a59ab947ffa6c816b4a174537f
institution OA Journals
issn 1000-436X
language zho
publishDate 2014-06-01
publisher Editorial Department of Journal on Communications
record_format Article
series Tongxin xuebao
spelling doaj-art-ffde13a59ab947ffa6c816b4a174537f2025-08-20T02:09:29ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-06-0135394659681972Seam-Carving forgery detection based on expanded Markov featuresGuo-rui SHENGTie-gang GAOLi FANLin GAOFu-sheng YANGShun ZHANGTo deal with the digital image forgery using Seam-Carving, a detection algorithm based on expanded Markov features was proposed. The algorithm takes full acount of the image frequency domain change caused by Seam-Carving operation, merges the features based on traditional and expands Markov transfer-probability matrix. Trained by SVM, the merged Markov feature can identify the Seam-Carving forgery more effectively. The experiment result shows that the performance of the proposed method is better than that of the method based on traditional Markov features and other existing methods.http://www.joconline.com.cn/thesisDetails#10.3969/j.issn.1000-436x.2014.06.006image forensics; image forgery;Seam-Carving;Markov feature;image resizing
spellingShingle Guo-rui SHENG
Tie-gang GAO
Li FAN
Lin GAO
Fu-sheng YANG
Shun ZHANG
Seam-Carving forgery detection based on expanded Markov features
Tongxin xuebao
image forensics; image forgery;Seam-Carving;Markov feature;image resizing
title Seam-Carving forgery detection based on expanded Markov features
title_full Seam-Carving forgery detection based on expanded Markov features
title_fullStr Seam-Carving forgery detection based on expanded Markov features
title_full_unstemmed Seam-Carving forgery detection based on expanded Markov features
title_short Seam-Carving forgery detection based on expanded Markov features
title_sort seam carving forgery detection based on expanded markov features
topic image forensics; image forgery;Seam-Carving;Markov feature;image resizing
url http://www.joconline.com.cn/thesisDetails#10.3969/j.issn.1000-436x.2014.06.006
work_keys_str_mv AT guoruisheng seamcarvingforgerydetectionbasedonexpandedmarkovfeatures
AT tieganggao seamcarvingforgerydetectionbasedonexpandedmarkovfeatures
AT lifan seamcarvingforgerydetectionbasedonexpandedmarkovfeatures
AT lingao seamcarvingforgerydetectionbasedonexpandedmarkovfeatures
AT fushengyang seamcarvingforgerydetectionbasedonexpandedmarkovfeatures
AT shunzhang seamcarvingforgerydetectionbasedonexpandedmarkovfeatures